P. S. Neelakanta, Dolores Degroff

Neural Network Modeling (eBook, PDF)

Statistical Mechanics and Cybernetic Perspectives

• Format: PDF

Produktbeschreibung

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 256
• Erscheinungstermin: 6. Februar 2018
• Englisch
• ISBN-13: 9781351428965
• Artikelnr.: 54039376

Autorenporträt

Neelakanta, P. S.; DeGroff, Dolores

Inhaltsangabe

Introduction. Neural and Brain Complex. Concepts of Mathematical
Neurobiology. Pseudo-Thermodynamics of Neural Activity. The Physics of
Neural Activity: A Statistical Mechanics Perspective. Stochastic Dynamics
of the Neural Complex. Neural Field Theory: Quasiparticle Dynamics and Wave
Mechanics Analogies of Neural Networks. Informatic Aspects of
Neurocybernetics. Appendices: Magnetism and the Ising Spin-Glass Model.
Matrix Methods in Little's Model. Overlap of Replicas and Replica Symmetry.
Bibliography. Index.